Chance-constrained AC OPF with probabilistic guarantees...
Transcript of Chance-constrained AC OPF with probabilistic guarantees...
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Chance-constrainedACOPFwithprobabilisticguaranteesandscalableperformance
MariaVrakopoulouMarieCuriePost-doctoralfellow,UCBerkeley
IncollaborationwithJenniferMarleyandProf.IanHiskens (UniversityofMichigan)
ControlofComplexSystems:AnIntegratedPerspectiveonModernPowerGridControlACCWorkshopMay22-23,2017
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Operator
Decisions
Load
time
PVpower
Demand
Contingencies
Decisionsunderuncertainty
Windpower
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time
100%
Powerflow
steadystatelimit
Decisionsunderuncertainty
Inthepast:• Notheavilyloadedsystem• LowshareofRenewableEnergy
Sources(RES)
Enoughmargintowithstanduncertainty
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Decisionsunderuncertainty
time
100%
Powerflow
steadystatelimitBut..
demand(higherloadingonthesystem)penetrationofRES(higheruncertainty)
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Decisionsunderuncertainty
time
100%
Powerflow
steadystatelimit
Operatingclosertothemargins
Maybenotpossibletowithstanduncertaintyorunnecessaryexpensivedesign
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NeedforreliablestochasticOptimalPowerFlow formulations
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q TheACOptimalPowerFlow(OPF)problem
q StochasticACOPFformulation
q Conclusions
orunnecessaryexpensivedesign
Outline
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q TheACOptimalPowerFlow(OPF)problem
q StochasticACOPFformulation
q Conclusions
orunnecessaryexpensivedesign
Outline
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GeneralACOPFmodel
subject to
quadratic cost of generation
powerflow equations
operationallimits
voltage magnitude and angle
windpowerforecast
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OPFchallenges
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• Nonconvexconstraints
• Needtobeappliedinlargenetworks
Different solution methodologies aim to find an approximate solution, a local minimum or ideally the optimal solution in a scalable manner
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OPFsolutionmethodologyexamples
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• ApproximationsDC OPF: linearized power flow equations - quadratic problem
• Finding the global optimumConvex Relaxations: Semidefinite program (SDP), Second order cone program (SOCP)
• Finding a local minimumAC-QP OPF : Quadratic program (QP) formulation - separate AC power flow based update
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OPFsolutionmethodologyexamples
ACC 2017 Chance-constrained AC OPF with probabilistic guarantees 5/30
• ApproximationsDC OPF: linearized power flow equations - quadratic problem
• Finding the global optimumConvex Relaxations: Semidefinite program (SDP), Second order cone program (SOCP)
• Finding a local minimumAC-QP OPF : Quadratic program (QP) formulation - separate AC power flow based update
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OPFsolutionmethodologyexamples
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• ApproximationsDC OPF: linearized power flow equations - quadratic problem
• Finding the global optimumConvex Relaxations: Semidefinite program (SDP), Second order cone program (SOCP)
• Finding a local minimumAC-QP OPF : Quadratic program (QP) formulation - separate AC power flow based update
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AC-QPOPFalgorithm
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[A. Wood, B. Wollenberg, and G. Sheble, Power Generation, Operation and Control, 3rd ed. John Wiley and Sons, Inc., 2013]
• Initialize the AC power flow using an SCOP solution• SOCP provides a lower bound of the OPF problem
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q TheACOptimalPowerFlow(OPF)problem
q StochasticACOPFformulation
q Conclusions
orunnecessaryexpensivedesign
Outline
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ACC 2017 Chance-constrained AC OPF with probabilistic guarantees 8/30
Steady state operating points
Givenageneration-loadmismatchtheautomaticcontrolloopswillleadtoanewpost-disturbanceoperatingpoint
Pre-disturbanceoperating point
Post-disturbanceoperating point
source:swissgrid
Example:Frequency controlSaferegion
PrimarySecondary Tertiary
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• AGCresponse(secondarycontrol)
Satisfytheconstraintsatthesteadystatepointafter
• aredispatch action(ortertiarycontrol)
• Primarycontrol
Steady state operating points
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Optimaldecision making under uncertainty
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source:swissgrid
timeScheduling point
• Optimaldecisions
• Uncertain forecast
• Historicaldata ofthe uncertainty
Deployment point
• Apply the optimaldecisions
• Realization of theuncertainty
Dayahead market:one day beforeIntra-day markethours before
Uncertaintymodel
Optimaldecisionmaking
Apply inrealtime
Schedulingpoint Deploymentpoint
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ChanceConstrainedOPF
subject to
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subject to
Satisfytheconstraintsinaprobabilisticsense
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ChanceConstrainedOPF
subject to
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subject to
Solvethechanceconstraintusingdata-basedoptimization
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Thescenarioapproach[1]• Noassumptionsontheunderlyingdistributionoftheuncertainty• Guaranteesa-priorithatthechanceconstraintwillbesatisfiedwithacertain
confidence• Requiresconvexitywithrespecttodecisionsvariables
Probabilisticallyrobustdesign[2]• Mixtureofrandomizedandrobustoptimization• Noassumptionsontheunderlyingdistributionoftheuncertainty• Guaranteesa-priorithatthechanceconstraintwillbesatisfiedwithacertain
confidence• Noparticularstructurerequiredwithrespecttothedecisionvariable
[1] G. Calafiore and M. Campi, TAC, 2006[2] K. Margellos, P. Goulart and J. Lygeros, TAC, 2014[3] M. C. Campi, S. Garatti, F. A. Ramponi, CDC, 2015
Data-basedoptimization
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Thenon-convexscenariooptimization[3]• Noassumptionsontheunderlyingdistributionoftheuncertainty• Applicabletocon-convexproblems• Providesa-posterioriguarantees
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• Substitutethechanceconstraintwithafinitenumberofhardconstraintsbasedonscenariosoftheuncertainty
• Howmanyscenariosdoweneedtoprovideprobabilisticguarantees?
Thescenario approach [1]
number ofdecision elementsconfidence
violationlevel
numberof scenarios
𝑁 ≥ #$
%%&#
ln #)+ 𝑁+ − 1
𝛿#
𝛿/
𝑖 = 1…𝑁for
Data-basedoptimization
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𝑖
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• Usethescenarioapproach tofind‘bounds‘oftheuncertaintyelements
• Howmanyscenariosdoweneedtoprovideprobabilisticguarantees?
Step1
number ofuncertaintyelements
confidenceviolationlevel
numberof scenarios
Probabilistically robustdesign(two-step approach)[2]
𝑁 ≥ #$
%%&#
ln #)+ 2𝑁5 − 1
𝛿#
𝛿/
Data-basedoptimization
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• Usethescenarioapproach tofind‘bounds‘oftheuncertaintyelements
• Howmanyscenariosdoweneedtoprovideprobabilisticguarantees?
Step1
number ofuncertaintyelements
confidenceviolationlevel
numberof scenarios
Probabilistically robustdesign(two-step approach)[2]
𝑁 ≥ #$
%%&#
ln #)+ 2𝑁5 − 1
𝛿#
𝛿/
Data-basedoptimization
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Step2
• Solvearobustreformulationoftheinitialchanceconstrainedproblem
• Anysolutionoftherobustproblemisfeasibleforthechanceconstrainedwithconfidenceatleast
forall 𝛿 ∈ ∆8
𝛿#
𝛿/
∆8
1 − 𝛽
Dealingwiththechanceconstraint
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Probabilistically robustdesign(two-step approach)[2]
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Priorwork
ACC 2017 Chance-constrained AC OPF with probabilistic guarantees 16/30
[M.Vrakopoulou,K.Margellos,J.Lygeros,andG.Andersson,“ProbabilisticguaranteesfortheN-1securityofsystemswithwindpowergeneration,”inPMAPS2012]
DCpowerflow• Usingactivepowerpolicies
allowtotriviallysatisfytheequalityconstraintsandtheproblemcanbetractable
• Methods[1]and[2]wereefficientlyused
• ButsolutionnotaccuratefortheACconstraints
Failstosatisfythedesiredviolationlevel
Satisfiesthedesiredviolationlevel
§ MonteCarloevaluationfor10000windpowerrealizations
IEEE30-busnetwork§ 4loadprofiles§ Desiredviolationlevel10%
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Priorwork
ACC 2017 Chance-constrained AC OPF with probabilistic guarantees 16/30
[M.Vrakopoulou,M.Katsampani,K.Margellos,J.Lygeros,andG.Andersson “Probabilisticsecurity-constrainedACoptimalpowerflow,”inIEEEPowerTech Conference,2013]
• SDPformulation,activepowerandgeneratorvoltagepoliciesneedtobeusedtomaketheproblemtractable.
• Method[2]wasapplied• Butscalabilityissues!!
ACpowerflow
IEEE14-busnetwork§ Desiredviolationlevel10%§ MonteCarloevaluationfor10000windpower
realizations
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Data-basedoptimization
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Thenon-convexscenariooptimization[3]
• UseNscenariosasaninputtothenon-convexoptimizationalgorithm• Calculatethesupportscenariosk oftheobtainedsolution• Providesa-posterioriguaranteesfortheupperboundofchanceconstraint
violationlevel
[3] M. C. Campi, S. Garatti, F. A. Ramponi, CDC, 2015
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Non-convexalgorithm
Data-basedoptimization
Supportscenarios
Non-convexalgorithm
k
CanwefindasmallnumberofscenarioskthatmustbeincludedintheproblemsothatthesolutionwouldbethesameastheoneusingalltheNscenarios?
ACC 2017 Chance-constrained AC OPF with probabilistic guarantees 18/30
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30ACC 2017 Chance-constrained AC OPF with probabilistic guarantees 19/30
StochasticAC-QPOPF
[J.F.Marley,M.VrakopoulouandI.A.Hiskens,AnAC-QPoptimalpowerflowalgorithmconsideringwindforecastuncertainty,IEEEPESInnovativeSmartGridTechnologies,ISGT-Asia2016]
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ScenariosintheOPF
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Casestudies
ACC 2017 Chance-constrained AC OPF with probabilistic guarantees 21/30
Test the stochastic AC-QP algorithm for the following IEEE benchmarknetworks:
• 14-Bus: 5 Generators, 20 Branches, 2 wind buses
• 30-Bus: 6 Generators, 41 Branches, 5 wind buses
• 57-Bus: 7 Generators, 80 Branches, 7 wind buses
• 118-Bus: 54 Generators, 186 Branches, 10 wind buses
N=1500scenariosβ=0.001
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Increasingthenetworksize
ACC 2017 Chance-constrained AC OPF with probabilistic guarantees 22/30
2
3
4
5
6
7
8
9
Num
ber o
f Sup
port
Scen
ario
s in
AC-
QP
pOPF
118 Bus w/ 10 Wind: Number of Support Scenarios
14 30 57 11810
20
30
40
50
60
70
Tota
l Exe
cutio
n Ti
me
(Sec
.)
118 Bus w/ 10 Wind: AC-QP Time
14 30 57 118
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Increasingthenetworksize
ACC 2017 Chance-constrained AC OPF with probabilistic guarantees 23/30
0
0.1
0.2
0.3
0.4
0.5
% o
f 10k
Sce
nario
s w
ith A
C V
iola
tions
118 Bus w/ 10 Wind: AC-QP Violation Probability (Empirical) - AC Power Flow
14 30 57 1182
2.5
3
3.5
4
4.5
Theo
retic
al P
roba
bilit
y of
Vio
latio
n
118 Bus w/ 10 Wind: Theoretical Probability of Violation
14 30 57 118
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IncreasingthenumberofscenariosN
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IncreasingthenumberofscenariosN
ACC 2017 Chance-constrained AC OPF with probabilistic guarantees 25/30
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IncreasingthenumberofscenariosN
ACC 2017 Chance-constrained AC OPF with probabilistic guarantees 26/30
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q TheACOptimalPowerFlow(OPF)problem
q StochasticACOPFformulation
q Conclusions
orunnecessaryexpensivedesign
Outline
![Page 39: Chance-constrained AC OPF with probabilistic guarantees ...events.pnnl.gov/pdfs/ccsi/session5/talk12.pdf · optimal power flow,” in IEEE PowerTechConference, 2013] • SDP formulation,](https://reader034.fdocuments.us/reader034/viewer/2022042405/5f1d4273d7168c75a16108f6/html5/thumbnails/39.jpg)
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Conclusions
TheAC-QPOPFmethodhasbeenextendedtoincludewindpoweruncertainty,throughtheadditionofafinitenumberofwindscenarios.
TheresultingstochasticAC-QPOPFalgorithmoffersseveraladvantages:
• ItdoesnotrelyuponmodelapproximationsandproducesanACfeasiblesolution.
• Itprovidesaprobabilisticallyrobustsolutionwitha-posterioriprobabilisticviolationguarantees.
• Thenumberofsupportscenariosremainsmallastheproblemsizeincreaseshighlightingpromisingscalabilityproperties
ACC 2017 Chance-constrained AC OPF with probabilistic guarantees 28/30